Context demographic determination system

ABSTRACT

Systems, methods, and devices for determining contexts and determining associated demographic profiles using information received from multiple demographic sensor enabled electronic devices, are disclosed. Contexts can be defined by a description of spatial and/or temporal components. Such contexts can be arbitrarily defined using semantically meaningful and absolute descriptions of time and location. Demographic sensor data is associated with or includes context data that describes the circumstances under which the data was determined. The demographic sensor data can include demographic sensor readings that are implicit indications of a demographic for the context. The sensor data can also include user reported data with explicit descriptions of a demographic for the context. The demographic sensor data can be filtered by context data according a selected context. The filtered sensor data can then be analyzed to determine a demographic profile for the context that can be output to one or more users or entities.

BACKGROUND

Various types of devices, sensors and techniques exist for determiningimplicit and explicit characteristics of people and places. Some systemsuse devices associated with a particular user to sense or determine userspecific information. Sensors in or coupled to a mobile electronicdevice can sense various implicit indicators of characteristics for aparticular user. For example, sensors in a smartphone can sense thephysical properties, e.g., position, temperature, rate of motion,heartbeat, etc., of a particular user of the device to gatherinformation that can imply characteristics for that particular user.Other conventional mobile electronic device based systems also gatherinformation about particular users by providing mechanisms through whicha user can explicitly report user characteristics, e.g., age, mood,state of health, weight, etc. For example, a smartphone can execute anapplication that prompts a user to explicitly enter personalinformation. These types of mobile implicit and explicit usercharacteristic collection devices only gather information for one userat a time. Typically, each mobile device only gathers information aboutthe owner or the current user of the device.

Other systems use stationary sensors, such as cameras, infrared imagers,microphones, voice recognition, etc., to detect the characteristics ofmultiple people in a particular area in proximity to the sensors. Suchsystems can analyze the physical properties of the people to determinecharacteristics, e.g., mood, health, or demographic information, for thepeople in that particular location. For example, systems exist that candetermine the mood, e.g., happy, content, sad, etc., of some portion ofthe people in a location based on the physical properties, such as thedegree to which a person is smiling, for people who come within range ofa particular sensor. Because the sensors in such systems are stationary,the results are limited to locations in which the sensors are installed.Furthermore, the resulting sample of a particular group or populationwithin range of the sensors is limited. The limited sampling of thegroup of people can skew the results when interpolating, or otherwisedetermining, the mood or other characteristics associated with a givenlocation.

FIG. 1 illustrates a diagram of a particular region 100. The region 100can include a number of locations 120 in which various numbers of people110 can be found. Some of the locations 120 can include a stationarysensor (SS) 115. As shown, the distribution of the stationary sensors115 is limited to only a few of the possible locations 120. Accordingly,only locations 120 that include a stationary sensor 115 are capable ofdetermining even an approximation of a characteristic, such as the mood,of some group of people 110 in a particular location 120 or region 100.In the specific example shown, only locations 120-1, 120-4, 120-6,120-10, and 120-12 include stationary emotion sensors 115. The otherlocations 120 have no means for reliably determining the characteristicsfor those locations.

Furthermore, even locations 120 that are equipped with a stationarysensor 115 are limited by the ability of the sensor to detect only alimited sample of the people 110 in the location. The limits of thestationary sensors 120 can be based on the limits of the sensor in termsof range, speed, and accuracy. In addition, some people may activelyavoid the stationary sensors 120. For instance, a mood detecting cameracan be positioned at the front door of a given entertainment venue tocapture the facial expressions of people as they enter the venue, andanother mood detecting camera can be positioned near the performancestage of the same venue to capture facial expressions of people as theywatch a performance. The facial expressions captured by the mooddetecting camera at the front door of the venue might detect that amajority of the people entering the venue are excited, and the facialexpressions captured by the mood detecting camera at the stage mightdetect that the majority of people near the stage are happy. However,there may be other people, or even a majority of people, in the venuenot being imaged by either of the mood detecting cameras, who may bebored, tired, or unhappy with the entertainment or the venue. In suchsituations, any interpolated result or conclusion as to the overall moodof the people in the venue can be spurious, and thus, not represent thetrue mood or success of the venue in entertaining its patrons.Embodiments of the present disclosure address these and other issues.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates conventional systems that use stationary sensorenabled electronic devices for determining limited characteristics forselect contexts.

FIG. 2A illustrates various types of sensor enabled electronic devicesthat can be used in various embodiments of the present disclosure.

FIG. 2B is a block diagram of the sensor enabled electronic device thatcan be used in various embodiments of the present disclosure.

FIG. 3 is a block diagram of a system for the deployment of multiplestationary and mobile sensor enabled electronic devices for determiningcharacteristics of various contexts, according to various embodiments ofthe present disclosure

FIG. 4 illustrates various definitions of contexts, according to variousembodiments of the present disclosure.

FIG. 5 illustrates the flexible definitions of contexts, according tovarious embodiments of the present disclosure.

FIG. 6 illustrates the combination of spatial and temporal components ina context, according to various embodiments of the present disclosure.

FIG. 7 illustrates changes in population and context characteristicsaccording to changes in a temporal component of a context definition,according to various embodiments of the present disclosure.

FIG. 8 is a flowchart of a method for defining contexts, according tovarious embodiments of the present disclosure.

FIG. 9 is a flowchart of a method for determining contextcharacteristics using sensor data received from multiple sensor enabledelectronic devices, according to various embodiments of the presentdisclosure.

FIG. 10 illustrates emotion sensor data associated with variouscontexts, according to embodiments of the present disclosure.

FIG. 11 illustrates tracking changes in a motion sensor data associatedwith various contexts, according to embodiments of the presentdisclosure.

FIG. 12 illustrates trends of individual user emotion based on changesin context, according to embodiments of the present disclosure.

FIG. 13 prediction of individual user emotions based on changes incontext, according to embodiments of the present disclosure.

FIG. 14 illustrates demographic sensor data associated with variouscontexts, according to embodiments of the present disclosure.

FIG. 15 illustrates changes in demographic sensor data associated withvarious contexts, according to various embodiments of the presentdisclosure

FIG. 16 illustrates health sensor data associated with various contexts,according to embodiments of the present disclosure.

FIG. 17 illustrates changes in health sensor data associated withvarious contexts, according to embodiments of the present disclosure.

DETAILED DESCRIPTION

Described herein are techniques for systems and methods for flexiblydefining a particular context and determining a characteristic for thatcontext using distributed sensor enabled electronic devices. Inparticular, embodiments of the present disclosure include determining ademographic profile for a context using demographic sensors indistributed stationary and mobile electronic devices. In the followingdescription, for purposes of explanation, numerous examples and specificdetails are set forth in order to provide a thorough understanding ofparticular embodiments. Particular embodiments as defined by the claimsmay include some or all of the features in these examples alone or incombination with other features described below, and may further includemodifications and equivalents of the features and concepts describedherein.

Various specific embodiments of the present disclosure include methodsfor determining a demographic profile for a context. Such methods caninclude receiving demographic data from multiple distributed electronicdevices. The demographic data can include context data and correspondingimplicit demographic data sensed by the plurality of distributedelectronic devices for multiple contexts. Some embodiments of the methodfurther include determining a first context, determining a first portionof the demographic data determined to include context data that matchesthe first context, analyzing the implicit demographic data in first theportion of the demographic data to generate demographic characteristicsfor the first context, and generating a first demographic profile forthe first context based on the demographic characteristics.

Other embodiments of the present disclosure include non-transitorycomputer-readable storage media containing instructions that, whenexecuted, control a processor of a computer system to be configured forreceiving demographic data from multiple distributed electronic devices.The demographic data can include context data and corresponding implicitdemographic data sensed by the plurality of distributed electronicdevices for a plurality of contexts. Such embodiments can also includedetermining a first context from the multiple contexts, determining afirst portion of the demographic data determined to include context datathat matches the first context, analyzing the implicit demographic datain first the portion of the demographic data to generate demographiccharacteristics for the first context, and generating a firstdemographic profile for the first context based on the plurality ofdemographic characteristics.

Various other embodiments of the present disclosure include anelectronic device that includes a processor, a demographic sensor, anelectronic communication interface, and a non-transitorycomputer-readable storage medium. The non-transitory computer-readablestorage medium can contain instructions that when executed, control theprocessor to be configured to activate the demographic sensor todetermine a demographic sensor reading, and determine context data forthe demographic sensor reading. The context data describes thecircumstances in which the demographic sensor reading was determined.The instructions can further control the processor to be configured togenerate demographic sensor data that includes the context data and thedemographic sensor reading, send the demographic sensor data to one ormore remote service providers through the electronic communicationinterface, and receive, from a first remote service provider in the oneor more remote service providers through the electronic communicationinterface, summary demographic sensor data for a particular context. Thesummary demographic sensor data may include demographic sensor data,received by the first remote service provider from a plurality of otherelectronic devices, and determined to include context data that matchesthe particular context.

Various embodiments of the present disclosure include systems, methods,and devices for determining contexts and determining a demographicprofile for those contexts using information received from multipledemographic sensor enabled electronic devices. Contexts can be definedby a description that includes spatial and/or temporal components. Thespatial components can refer to various types of absolute and relativelocation description systems, such as coordinate based maps systems andproximity based location services. The temporal components can referenceabsolute and relative time description systems. Such time descriptionsystems can include a start time and date, a stop time and date, or adesignation of some particular time period within some proprietary oruniversal time keeping system. In some embodiments, the context can bedetermined by the presence, concentration, or availability ofdemographic sensor data for a particular time and place. Accordingly,contexts can be arbitrarily defined as individual and compositecombinations of time and location.

Once the context is selected or defined, all or some of the demographicsensor data received from multiple electronic devices can be filtered oranalyzed to determine some portion of the demographic sensor data thatincludes or is associated with context data that matches the selectedcontext. The context data can include temporal and spatial componentsthat can describe the circumstances under which demographic sensorreadings included in the sensor data were sensed, recorded, or otherwisedetermined. In some embodiments, the demographic sensor data can includeimplicit indications of demographic characteristics and explicitdescriptions of demographic characteristics. The implicit descriptorscan include processed or unprocessed demographic sensor readings. Suchsensor readings can be mapped to a particular demographic or demographicprofile. The explicit descriptions of demographic characteristics caninclude one or more user reported points of data regarding a demographiccharacteristic for a context, e.g., a demographic characteristicreported by a user through a particular application, website, or socialmedia network. As used herein, the term “demographic sensor” can referto any sensor that may be used to sense information that can be used toinfer a demographic or a demographic characteristic, regardless ofquality or accuracy. For example, a blood pressure monitor might be usedto indicate a demographic characteristic of a person, or might be usedin conjunction with the data from other sensors to infer a demographiccharacteristic of one or more people.

The demographic sensor data determined to be received from demographicsensor enabled electronic devices that are or were in the context ofinterest can be analyzed to determine a demographic profile for thecontext. There are many forms that the resulting demographic profilescan take and can be based on the needs of the users or entities thatwill be consuming or viewing the demographic profiles. For example, thedemographic profile can include a complete listing of all demographicsensor data for the context. In other embodiments, the demographicprofile can include summaries of the most frequent demographiccharacteristic indicators and descriptions in the sensor data for thecontext. In one embodiment, the demographic profile can include anaggregation of all of the demographic indicators into a single,aggregate demographic indicator. Regardless of the format of thedemographic profile, the profiles can be output over various channelsand lines of communications. For example, the demographic profiles andthe related contexts can be published to a website, sent as an email,broadcast in text messages, or pushed using a Really Simple Syndication(RSS) feed.

Various embodiments of the present disclosure will now be described inmore detail with reference to specific devices, systems, and use cases.

Sensor Enabled Devices

A significant portion of users encounters or uses at least oneelectronic device on a daily basis. Any or all such devices can beconfigured to include one or more varieties of sensors. FIG. 2Aillustrates several examples of sensor enabled electronic devices 210.Some sensor enabled devices 210 are mobile devices (referred to assensor enabled mobile electronic devices 210) that many users carrynearly every day. These devices include various types and brands ofsensor enabled mobile telephones 210-1, smart phones 210-2, tabletcomputers, and laptop computers, etc. While mobile computing andcommunication devices are some of the most commonly used devices, thereare other sensor enabled mobile electronic devices 210 that are alsooften used. For instance, various users carry sensor enabled pedometers,electronic music players (e.g., MP3) 210-3, watches 210-4, glasses, and,on occasion, specialty mobile electronic devices, like self-guidedposition-sensitive museum tour devices. In addition, there areconfigurations of mobile electronic devices in which one device can betethered to or connected to another device. For example, a watch 210-4or watch 210-5, can be connected to a smart phone 210-2 by a wired orwireless connection to share information, computing, networking, orsensor resources.

Any of the coupled or individual sensor enabled mobile electronicdevices 210 may include one or more types of sensors, such asenvironmental, body, or location sensors. The mobility of such devicesprovides for flexible deployment of sensors into a wide range ofcontexts to determine various characteristics about those contexts. Inaddition, there may be some contexts that are equipped with one or moretypes of sensor enabled stationary devices (referred to as sensorenabled stationary electronic devices 210), shown generically at 210-6,that can be installed or placed in various contexts for detectingphysical properties, e.g., temperature signatures, sound levels, facialexpressions, etc., of people and conditions in those contexts. Theinformation determined or sensed by stationary electronic devices 210-6can be used independently or in conjunction with the informationcollected from other mobile and stationary sensor enabled devices.

FIG. 2B illustrates a schematic of a sensor enabled electronic device210 that can be used in implementations of various embodiments of thepresent disclosure. As discussed above, sensor enabled electronic device210 can be a mobile or a stationary device. Either type of electronicdevice can include an internal communication bus 219, through which theconstituent components of the electronic device 210 can communicate withand/or control one another. In some embodiments, electronic device 210can include an internal sensor 215 and/or an external sensor 216. Thesensors can include any type of sensor capable of detecting a physicalcharacteristic of a person, object, or environment. In some embodiments,the external sensor 216 can be coupled to the electronic device 210 by awired or wireless connection. Accordingly, the external sensor 216 canbe configured to sense a region, object, or a part of a user's body thatis separate from the electronic device 210. For example, the externalsensor 216 can be included in a wrist watch, a pair ofspectacles/goggles, or a body monitor that can be attached or affixed toa part of the user's body, e.g., a thermometer or heart rate monitor.

Each of the sensors can be controlled by the processor 214 executingcomputer readable code loaded into memory 213 or stored in thenon-transitory computer readable medium of data store 218. Readingssensed by the external sensor 216 and internal sensor 215 can becollected by the processor 214 and stored locally in the memory 213 orthe data store 218. In some embodiments, the readings from the externalsensor 216 and/or the internal sensor 215 can be sent to remote serviceprovider 230. In such embodiments, electronic device 210 can include acommunication interface 212 for translating or converting the readingsfrom the sensors from one format to another for transmission using thecommunication transmitter/transceiver 212 and network 220. Accordingly,electronic device 210 can be configured to communicate with network 220and service provider 230 using a variety of wired and wirelesselectronic communication protocols and media. For example, electronicdevice 210 can be configured to communicate using Ethernet, IEEE802.11xx, worldwide interoperability for my quick access (WiMAX),general packet radio service (GPRS), enhanced data rates for GSMevolution (EDGE), and long-term evolution (LTE), etc. The readings fromthe sensors, or sensor data that includes or is generated using thesensor readings, can be sent to the service provider 230 in real time.Alternatively, sensor readings or sensor data can be stored and/or sentto the service provider 230 in batches or as network connectivityallows.

In some embodiments, the sensor enabled electronic device 210 can alsoinclude a location determiner 217. The location determiner 217 can,through various methods and technologies, e.g., global positioningsystems (GPS), near field communication (NFC), proximity sensors, etc.,determine the location and movement of electronic device 210. In someembodiments, the location determined by the location determiner 217 canbe included or associated with sensor readings from the external sensor216 and/or the internal sensor 215 in sensor data sent to serviceprovider 230. As used herein, the term sensor data is used to describeany data that includes or is associated with sensor readings and/or userreported data. For example, in some embodiments, sensor data can includethe sensor readings and user reported data, along with the time, date,and location at which the sensor readings were taken or the userreported data was collected. The sensor data can also include any otherconditions or exceptions that were detected when the correspondingsensor data was determined.

Deployment of Sensor Enabled Devices

FIG. 3 illustrates a schematic of a system 300 that includes many sensorenabled electronic devices 210 deployed in multiple contexts 410. Thesensor enabled electronic devices 210 can be implemented as stationaryor mobile devices. As such, the stationary devices can be explicitlyassociated with a particular location or event. For example, sensorenabled electronic device 210-1 can be a stationary device equipped witha camera, or other sensor, installed in a specific context, 410-1, suchas a particular location or in a particular vehicle (e.g., a bus, train,plane, ship, or other multi-person conveyance).

In another example, some sensor enabled electronic devices 210 can bedeployed passively. For example, sensor enabled mobile devices 210 canbe passively deployed into multiple contexts by simply observing whereusers take their associated mobile devices. Passive deployment of thesensor enabled electronic devices 210 refers to the manner in which thedevices are carried with users into whatever context the users choose.Accordingly, there is no central entity that is directing where eachsensor enabled mobile electronic device 210 will be located or where itwill go next. That decision is left up to individual users of the sensorenabled mobile electronic devices 210. Accordingly, sensor enabledmobile electronic devices 210-2 and 210-3 can be observed to be in aparticular context 410-2, such as a location, at one time, but can thenbe observed in a different location at another time. Various advantagesthat can be realized due to the passive deployment of many sensorenabled mobile devices 210 will be described in reference to variousexamples below.

In some embodiments, each sensor enabled electronic device 210 mayinclude one or more sensors or measurement devices for detecting,recording, or analyzing the characteristics of one or more users,locations, or time periods. For example, each sensor enabled electronicdevice 210 can include a light sensor, a microphone, decibel meter, anaccelerometer, a gyroscope, a thermometer, a camera, an infrared imager,a barometer, an altimeter, a pressure sensor, a heart rate sensor, agalvanic skin response sensor, a vibration sensor, a weight sensor, anodor sensor, or any other specialized or general purpose sensor todetect characteristics of a particular user of a particular device orother users, areas, or objects in the vicinity of the device. Asdiscussed above, the sensor enabled electronic devices 210 can alsoinclude location determination capabilities or functionality, e.g., aglobal positioning system (GPS), proximity detection, or InternetProtocol (IP) address location determination capabilities. In suchembodiments, sensor data collected by the various sensors can beassociated with a particular user and/or the particular location inwhich the sensor data was recorded or otherwise determined. In oneembodiment, the sensor data can also include time and/or dateinformation to indicate when the sensor data was captured or recorded.As used herein, any data referring to time, date, location, events,and/or any other spatial or temporal designation, can be referred to ascontext data. Accordingly, any particular sensor data can be associatedwith and/or include context data that describes the circumstances underwhich the sensor data was determined.

As shown in FIG. 2B, each of the sensor enabled electronic devices 210can also include electronic communication capabilities. Accordingly, thesensor enabled electronic devices 210 can communicate with one anotherand various service providers 230 over one or more electroniccommunication networks 220 using various types of electroniccommunication media and protocols. The sensor enabled electronic devices210 can send, and the service providers 230 can receive, sensor data(SD) associated with various particular users and contexts. The serviceproviders 230, using one or more computer systems, can analyze thesensor data to determine a characteristic of a particular context.

In various embodiments of the present disclosure, the various serviceproviders 230 can analyze the sensor data to determine mood, health,well-being, demographics, and other characteristics of any particularcontext 410 for which the service providers have sensor data. Theservice providers may then broadcast or selectively send the determinedcharacteristics data (CD) for a particular context 410 to one or more ofthe sensor enabled electronic devices 210, as well as to otherconsumers. Such embodiments will be described in more detail below.

Determining Contexts

As discussed herein, context can be defined by a geographical area andtime period at various levels of granularity. Accordingly, context caninclude predefined locations, such as a bar, restaurant, or amusementpark during a particular predetermined time period or event. When usingpredetermined or physical locations, the address or other semanticallymeaningful designation of the location can be associated with a range ofcoordinates that are observable by the sensor enabled devices. Incontrast, a context can be arbitrarily defined as any region or timeperiod for which sensor data is available. For example, a serviceprovider 230 can filter sensor data received from multiple sensorenabled electronic devices 210 for the sensor data associated with aspecific context of interest, e.g., a specific neighborhood, street,park, theater, nightclub, vehicle, or event. Once the sensor data isfiltered to isolate sensor data that includes context data that matchesor is associated with specific context 410 that the service provider isinterested in, the sensor readings in the sensor data can be analyzed todetermine or interpolate a particular characteristic for that particularcontext 410.

FIG. 4 illustrates how a region 400 can include a number of sub regions,or contexts 410, defined by a semantically meaningful geographicdesignation, like an address or venue name. As depicted, region 400 canbe segmented into a number of physical locations 120 and contexts 410 bywhich the context data can be filtered or grouped. Area 400 mayrepresent a city, a neighborhood, a business district, an amusementpark, etc., or any sub region thereof. Region 400 can be furthersegmented into individual and composite contexts 410. For example,context 410-1 can include a city block of locations 120-1 through 120-5,e.g., a block of buildings or businesses, in a particular neighborhoodof region 400. In some embodiments, each location 120-1 to location120-5 can be a particular context. However, as shown, the context 410-1can comprise all of the indoor space of locations 120-1 through 120-5,as well as any surrounding outdoor space, i.e., outside courtyards,sidewalks, and streets. Accordingly, by defining the area in and aroundlocations 120-1 to 120-5 as a particular context 410-1, variousrepresentations about that context can be determined by analyzing thesensor data received from the sensor enabled devices determined to be inarea 410-1. In one embodiment, a server computer of a service provider230 can filter the sensor data by the GPS coordinates to determine whichdevices are or were in context 410-1. In other embodiments, the serviceprovider may reference a semantically meaningful geographic locationfrom social media check-in information included in the sensor data,e.g., a user may self-report that he or she is dining at a restaurant atlocation 120-1 or exercising at a gym 120-4 inside context 410-1.

As shown, context 410-1 can also include a number of sub-contexts, suchas contexts 410-2 and 410-3 that can be defined by a physical locationand time period. For example, context 410-2 can be defined by physicallocations 120-3 and 120-3 between 9 am and 8 pm during some particularrange of dates, e.g., a sale event. Similarly, context 410-3 can bedefined by the physical location 120-5 on a specific night of a specificday of the year, e.g., a special event like a wedding or a concert.Using the definitions of the specific contexts of interest, particularembodiments can filter or sort the received sensor data to isolate andanalyze the relevant sensor readings to make determinations about thecharacteristics of the people 110 in the particular contexts 410. Forexample, the sensor data for context 410-2 may indicate that themajority of the people in the context are “happy”, while sensor data oruser reported data for context 410-3 can indicate that the median age ofthe people in the context is 45 years old.

Similarly, context 410-4 can be defined to include location 120-6, thesurrounding area of location 120-6, and the stationary sensor 115-3 on aparticular night of the week, e.g., every Wednesday night. By includingthe stationary sensor 115-3, a server computer analyzing the sensor datafrom sensor enabled mobile electronic devices 210 associated with thepeople 110 in context 410-4 can incorporate sensor data from thestationary sensor 115-3. In such embodiments, the sensor data fromsensor enabled mobile electronic devices 210 or the stationary sensor115 can be weighted according to determined relevancy, reliability,recentness, or other qualities of the sensor data. Additionally, therelative weights of the sensor data received from the mobile andstationary devices can be based on predetermined thresholds regardingsample size. If sensor data is received from some threshold number ofsensor enabled mobile electronic devices 210 in context 410-4, then thesensor data received from the stationary sensor 115-3 can have lessweight in the conclusions about the characteristics of the context. Incontrast, if only a few people in context 410-4 who are carrying sensorenabled mobile electronic devices 210 or there are only a few people inattendance, then the sensor data from stationary sensor 115-3 can bemore heavily weighted. Sample size is just one example factor by whichsensor data from mobile and stationary sensor enabled devices can beweighted relative to one another. Weighting sensor data according tovarious factors will be discussed below in more detail.

While the use of existing addresses and other semantically meaningfuldescriptions is a convenient way to define a particular context, someembodiments of the present disclosure allow for defining contexts thatare not necessarily associated with a particular physical location 120,such as a building or a venue. For example, context 410-5 can be definedin an open space that may or may not include a stationary sensor 115-5.For example, context 410-5 can include a parking lot or municipal parkwith no definite physical boundaries. By filtering sensor datadetermined to include geographic information for a particular area ofinterest, particular embodiments can flexibly define contexts to includegeographic locations of any size or shape. In some embodiments, thegeographic locations in a particular context can be defined by a rangeof GPS coordinates.

Since a service provider can arbitrarily define a context, anypreviously defined context can be redefined at any time as needed.Accordingly, contexts 410-1 and 410-2 shown in FIG. 4 can bereduced/merged into context 410-6 show in FIG. 5. Similarly, context410-5 shown in FIG. 4 can be divided into multiple contexts 410-9 and410-10 as shown in FIG. 5 to obtain greater granularity in the sensordata associated with the larger context 410-5. For instance, the context410-5 may originally have been defined around a large outdoor publicspace, but for a particular event, like a county fair or festival, maybe divided to be centered around featured events or exhibits, such as aperformance stage or art installation. Indoor spaces that define acontext, such as location 120-6, which defined context 410-4 in FIG. 4,can also be divided into smaller contexts, like context 410-7 and 410-8as shown in FIG. 5. In addition, new contexts can be added. Context410-11 can be added in and around location 120-13 when a particularservice provider or user requests or requires sensor data or acharacteristic determination for that particular context. For example, anew restaurant or bar may have opened that an advertiser would like toknow about.

As previously mentioned, the context can be defined by a combination ofspatial and temporal coordinates. FIG. 6 illustrates one particularcontext 410-14 that may include designations of particular locations120-11, 120-12, and 120-13, a particular day 615 of a particular month610 at a particular time 620. As shown, context 410-14 can include anynumber of people 110 who may or may not be carrying one or more sensorenabled mobile electronic devices 210. Assuming that some portion of thepeople 110 are carrying sensor enabled mobile devices 210, then aservice provider can receive sensor data for context 410-14. In someembodiments, the service provider can filter sensor data received frommany sensor enabled mobile electronic devices 210 by analyzing thecontext data included in the sensor data to determine which sensor datais associated with or captured within the spatial and temporalboundaries of the context 410-14. For example, context 410-14 caninclude an event, e.g., a grand opening, occurring in multiple buildings120-11, 120-12, and 120-13 on April 10, at 12:45 PM (−8 GMT). Theservice provider can then filter the sensor data for context data thatmatches the specific parameters with some degree of freedom, e.g., plusor minus 1 hour. The service provider can then analyze the sensorreadings in the sensor data determined to match the specific parametersof the event to determine one or more characteristics of the event.While analysis of the sensor data for individual contexts is helpful forcharacterizing a particular context, it is often helpful or informativeto understand how various characteristics change from context tocontext.

In some embodiments, the service provider 230 can determine a differencebetween a characteristic determined for one context and thecharacteristic determined at another context. For example, the serviceprovider 230 can compare the median age of people 110 in context 410-14,with the median age of people 110 in context 410-15 shown in FIG. 7. Inthe specific examples shown in FIGS. 6 and 7, the physical locations120-11, 120-12, and 120-13 of context 410-14 and context 410-15 are thesame. However, the time 720 and date 715 of context 410-15 are differentfrom the time 620 and date 615 of context 410-14. By analyzing thedifference in characteristics for each of the contexts, the serviceprovider can determine specific changes or trends. For example, a servercomputer, based on analysis of sensor data determined to match contexts410-14 and 410-15, can determine that the average age and the overallattendance increased between April and June of a particular year. Whilethe example shown in FIGS. 6 and 7 refers to two stationary locations,other embodiments of the present disclosure include contexts that aredefined by the interior space of multi-person conveyances, such asplanes, trains, boats, and buses.

FIG. 8 is a flowchart of a method for determining a particular contextand sensor data received from sensor enabled devices for that context.At 810, a service provider 230 can reference a semantically meaningfulsystem of context descriptions. As described herein, a context can bedefined by a location, a time period, or a combination thereof.Accordingly, the definition of a context may include a spatial componentmade in reference to the semantically meaningful system of contextdescriptions. For example, the context description can reference a mapwith a layout of predefined locations. The map can represent amunicipality with land lots or buildings identified by a system ofstreet addresses or lot numbers. Such municipal maps can includegeographical survey data that specifies the metes and bounds of variouslocations. Semantically meaningful systems of context description canalso include maps of individual properties, such as amusement parks,shopping centers, fair grounds, universities, schools, touristdestinations, etc. In such embodiments, a map of an individual propertymay include absolute or relative positions of features, objects, oramenities on the property. In addition, a semantically meaningful systemof context description can also include a temporal component, such as anevent calendar or schedule of events. Accordingly, the temporalcomponent can be combined with the spatial component to describe aparticular time and a particular location.

In 820, the service provider 230 can select the context from thesemantically meaningful system of context descriptions. As discussedabove, the selected context can include a temporal and a spatialcomponent. In 830, the service provider 230 may convert the selectedcontext from the semantically meaningful system of context descriptionsto an observable system of context descriptions. In such embodiments,the absolute or relative temporal and spatial components of the selectedcontext can be translated into observable spatial components and/orobservable temporal components. The observable spatial and temporalcomponents can reference a system that individual sensor enabledelectronic devices 210 can observe or sense. For example, the observablespatial components can be defined according to systems for positionlocation determination, e.g., global positioning systems (GPS) or beaconproximity location systems. In one embodiment, a street address for aparticular public park can be translated into a set of geographiccoordinates that describe the boundaries of the park. Similarly,temporal components can be defined according to a universal or commonclock or calendar, such as Greenwich Mean Time (GMT) or the Gregoriancalendar. In such embodiments, the name of an event, e.g., a concert canbe translated into a period of time that includes a starting time anddate and ending time and date along with a particular venue locationdefined in geographic coordinates. In other embodiments, each individualsensor enabled electronic device 210 can translate the observablespatial and temporal components of the context in which it determinessensor readings into a semantically meaningful system of contextdescriptions. For example, a sensor enabled smartphone can take anambient noise reading at a particular set of coordinates as determinedby the smartphone's GPS capabilities. The smartphone can then referencean internal map of nearby music venues to determine a particular venuebased on the determined coordinate. The smartphone can then associatethe ambient noise reading with that venue. In such embodiments, thecontext data in the sensor data can include the reference to thesemantically meaningful system of context descriptions.

In some embodiments, at 840, the service provider 230 can filter sensordata received from multiple sensor enabled electronic devices 210according the converted context description, i.e., the observablespatial and temporal components of the context description. Accordingly,filtering the sensor data may include determining sensor data thatincludes context data that matches the converted context description.

On occasion, the sensor data determined to include context data thatmatches the converted context description may not represent asatisfactory sample size. In such scenarios, various embodiments of thepresent disclosure can trigger an alert to indicate that the portion ofthe sensor data determined to match the converted context description isinsufficient for determining one or more characteristics for thecontext. When there appears to be too little sensor data to determine areliable characteristic for the context, it is possible to increase thesample size by expanding the context definition, e.g., increasing thegeographic region and/or time period of the context. If expanding thecontext definition does not result in a sufficient sample size, but itis also possible to rely on or re-weight explicitly reported contextcharacteristic descriptions. For example, when the sample size of thesensor data is insufficient to interpolate a reliable characteristic,then the interpolated characteristic can be weighted less than anyavailable user reported characteristic data when determining combinedcharacteristic data.

Determination of a Characteristic of a Context

Various embodiments of the present disclosure include systems andmethods for determining a particular characteristic of a context. Forexample, FIG. 9 is a flowchart of a method 900 for determining one ormore characteristics of a context using sensor data from multiple sensorenabled electronic devices 210. As used herein, the sensor data caninclude sensor readings as well as user reported data regarding aparticular characteristic of interest. In such embodiments, the sensorreadings can represent implicit context characteristic descriptions.Also, the user reported data can represent explicit contextcharacteristic descriptions. As shown, method 900 can begin at 910, inwhich a service provider receives sensor data from multiple sensorenabled electronic devices. The sensor data can include implicit andexplicit context characteristic data determined for many differentcontexts. As discussed above, the sensor enabled electronic devices 210can include both mobile and stationary electronic devices. At 920, theservice provider 230 may determine a portion of the sensor data thatincludes context data that matches the context description for aparticular selected context. In one embodiment, received sensor data canbe filtered to find only the sensor that includes context data thatindicates that the sensor readings or user reported data was determinedwhile the source sensor enabled electronic devices were in the selectedcontext. In one embodiment, user reported data can also includeinformation and characteristics reported by users using other devicesand applications, such a web browser executed on an internet-enabledesktop computer or reported to a service provider operator over a landline telephone.

At 930, once the portion of the sensor data associated with the selectedcontext is determined, the sensor readings and/or the user reported datacan be analyzed to determine a characteristic of interest for theselected context. Analyzing the sensor data can include mapping theimplicit context characteristic indications in the sensor readings tocorresponding context characteristics. The mapping from the implicitcontext characteristic indications to the corresponding characteristicscan be predetermined and based on prior analysis performed by theservice provider 230. Analyzing the sensor data can also includecomparing the mapped corresponding context characteristics with theexplicit context characteristic descriptions from the user reported datain the sensor data. When both implicit and explicit contextcharacteristic data are used, the implicit and explicit components canbe weighted according to observed or determined reliability of the data.The reliability of the implicit and explicit components can be based onthe timeliness, frequency, or consistency of similar sensor datareceived from each particular sensor enabled electronic device 210.Accordingly, sensor data received from devices that are considered to bemore reliable that other devices can be given more weight whendetermining the context characteristic. Similarly, implicit and explicitcomponents of the context characteristic descriptions can be weighteddifferently based on perceived reliability. For example, if the samplesize of the implicit components is considered to be too small to bereliable, then the explicit components can be given more weight. Incontrast, if the explicit components seem to be spurious or inconsistentwith other available data, then the implicit components can be givenmore weight when determining the characteristic of the context.

At 940, once the characteristic or characteristic profile for theselected context is determined, it can be output for use by varioususers and entities. For example, the form of the output characteristiccan include a recommendation or alert regarding the associated contextsent to one or more mobile electronic devices. Similarly, the outputcharacteristic for the context can be published to a website, along withother output characteristics for other contexts, or broadcast via emailor by RSS. In some embodiments, the output characteristic for thecontext can include tracking changes or trends of the particularcharacteristic over a number of context parameters, e.g., over time.Accordingly, changes in the characteristic can be analyzed as a functionof a change in context. The change in context can include changes in thetemporal and/or spatial components of a particular context. For example,the mood, average age, or wellness of a particular weekly event that mayinclude occasional changes in starting time and venue can be tracked asa function of start time or location. In one embodiment, users cansearch for contexts with certain characteristics or browse throughcontexts based on the context and/or the associated characteristics.

Specific examples of context characteristic determination with referenceto emotion, demographic, and health characteristics for particularcontexts will be discussed in more detail in reference to FIGS. 10 to 17below.

Determination of an Emotion for a Context

Various embodiments of the present disclosure include systems andmethods for determining an emotion or emotion profile for particularcontexts. FIG. 10 illustrates a scenario 1000 with two stationarylocation-based contexts 1005 and 1015, and one mobile location-basedcontext 1025, e.g., a public bus. In the particular example shown,context 1005 is a building at the corner of an intersection and context1015 is another building on the same street. Each of the buildings canbe associated with an address or a business name included in asemantically meaningful system of context descriptions. Scenario 1000also includes a context 1025 defined as the interior of a public or aprivate bus. In some embodiments, context 1025 can be defined not onlyas the interior of a particular bus, but as the interiors of some or allbuses servicing a particular route or line during some time period ofthe day.

A service provider 230 may receive emotion sensor data that includesimplicit and explicit indications of emotions from sensor enableddevices in any of the contexts 1005, 1015, and/or 1025. The implicit andexplicit indications of emotions can be mapped to or represent anemotional characteristic of one or more people in a particular context.Such emotional characteristics can include any number of emotionalstates, such as happiness, sadness, pensiveness, fear, anger, etc. Inthe example shown in FIG. 10, the emotion sensor data can includeindications of emotions that range from sadness 1011, happiness 1012,and excitement 1013. While this particular example of possibleindications of emotions in the emotion sensor data is limited to threeindications of various emotions, other embodiments of the presentdisclosure can include fewer or more possible indications of simple orcomplex emotions. The level of granularity and range of possibleemotions need not be limited.

By analyzing the emotion sensor data for the contexts, the serviceprovider can determine an associated emotion or emotion profile. Thestyle and format of the reported emotion or emotion profile for aparticular context can be suited to the needs of the users or otherentities that will be using the emotion characterization of the context.For example, when the emotion sensor data associated with context 1005is analyzed, it can be determined that there are more implicit and/orexplicit indications of happiness 1012 and excitement 1013 thanindications of sadness 1011. In this particular example, the serviceprovider 230 can determine that the context 1005 is trending as “happy”.In another embodiment, when the emotion sensor data associated withcontext 1015 is analyzed, it can be determined that 40% of the peopleare happy, 40% of the people are excited, and 20% of the people are sad.Similarly, by analyzing the emotion sensor data associated with context1025, it can be determined that the general mood of context 1025 is“sad”.

In some embodiments, when it is determined that a particular context isassociated with a specific emotion, the emotion can be used as anindication that something is occurring or has occurred, or to predictthat something is about occur. For example, when context 1025 isdetermined to be “sad”, it can indicate that the bus has experienced atraffic accident or is otherwise experiencing long delays. Similarly,when is determined that all or a majority of the emotion sensor data fora particular context includes indications of happiness, such informationcan be used as an indication that something has gone favorably, e.g., asuccessful event is occurring. While characterizations of the emotionfor a context that includes static or one time summaries are useful forsome purposes, it is often useful to also include analysis of thechanges in the emotion or emotion profile for a context over one or morespatial or temporal components of the context.

For example, FIG. 11 illustrates a scenario 1100 in which trends orchanges in the emotion sensor data can be observed. As shown, at time1105-1, the emotions for contexts 1005, 1015, and 1025 can becharacterized as shown in FIG. 11. However, after some amount of time,e.g., 2 hours, at time 1105-2, the emotion sensor data received fromvarious sensor enabled electronic devices 210 in context 1005 can beanalyzed to determine that the context is trending “sad”. This isbecause additional indications of a sad emotion have been received inthe last 2 hours. Also, at time 1105-2, the emotion sensor data fromdevices determined to be in context 1015 can be analyzed to determinethat the context is 23% “happy”, 66% “excited”, and 11% “sad”. Inreference to the context of the bus line or route 1025, the emotionsensor data can be analyzed to determine that people on the bus aregenerally happy. The changes in the emotions or emotion profiles for thecontexts 1005, 1015, and 1025 can be tracked and the changes or thetrends can be included in the output regarding emotion or emotionprofile for each context. For example, a some particular time, context1005 may be characterized as “sad” but, based on the recent trends inthe emotion sensor data for the context, it may be experiencing a changein the predominate mood from sad and trending toward “happy”.

While trends in context emotion over time are useful for some analysis,some embodiments include determining trends in context emotion accordingto changes in physical location. For example, context 1025 of the buscan include not only the interior of the bus, but can also includeenvironments through which the bus travels. Accordingly, trends inemotion can be tracked over changes in the buses position along itsroute. For example, the emotion of the bus context 1025 can change from“happy” while the bus is traveling through a nice part of town withlittle traffic to “sad” when the bus starts traveling through anotherpart of town with heavy traffic. Other aspects of the context 1025 ofthe bus can also be tracked. For example, changes in drivers, operators,tour guides, ambient music, dynamic advertising (video screen monitorsor public announcements), lighting, cleanliness, speed of travel, styleof driving, condition of the road, etc. can all be included in thecontext 1025 and cross-referenced with the emotion sensor data receivedfrom the sensor enabled electronic devices to determine the impact ofsuch individual and combined changes on the mood of the context. Inparticular example shown in FIG. 11, the bus context 1025 has beendescribed in detail, however other multi-person conveyances andtransportation routes can also be used to define a particular context.For example, other contexts can include stretches of freeway, airlineroutes, train routes, subway lines, sections of road, etc. for whichemotion sensor data can be analyzed to determine an associated emotionor an emotion profile.

Other embodiments of the present disclosure include tracking trends inemotion for individual users. In such embodiments, sensor enabled mobileelectronic devices 210 can be associated with particular users. Emotionsensor data, and other sensor data, received from such devices can alsobe associated with individual users. As a user moves from one context tothe next context, changes in that user's emotion can be tracked. Forexample, FIG. 12 shows emotion trend profiles 1110 that track theemotional changes for individual users 110 as they move from one contextto another. As shown, profile 1110-1 tracks the emotion or mood of auser 1 as he or she goes from context to context. Once some amount ofemotion sensor data for a particular user 1 in a variety of contexts iscollected, various embodiments of the present disclosure can beginpredicting how a user's mood will change if he or she goes from oneparticular context to another particular context.

FIG. 13 illustrates embodiments of the present disclosure can referencethe emotion trend profiles 1110 to predict a change in emotion forindividual users in various scenarios as they move from one type ofcontexts to another type of context. Based on the emotion trend profile1110 for each individual user, various predictions about the change in auser's mood are represented according to shifts in context from astarting context 120-X. If one particular user moves from startingcontext 120-X to another context, such as 120-1, then, based on theemotion trend profile 1110 for that user, it can be predicted that theuser's mood will change or stay the same. In the example shown, varioususers who begin as being happy in context 120-X can be predicted toremain happy, become excited, or be saddened when moved into one of theother contexts 120. Similarly, a user who begins as being sad in context120-X can be predicted to remain sad, or become happy or excited whenmoved into one of the other contexts 120.

In some embodiments, the prediction of a particular change in a user'smood can include consideration of current or historic determinations ofthe emotion of the context into which the user is about to enter. Forexample, a prediction can be made about whether a particular user willbe happy if he or she attends a particular event at a particularentertainment venue that is typically lively and happy. If trends in theuser's profile 1110 indicate a favorable mood change when going intosuch a context, then a prediction can be made that the user will enjoythe change in context. Based on such predictions, recommendations and/oralerts can be sent to the user via his or her associated sensor enabledmobile electronic device 210 when it is determined that the user iswithin some proximity to particular context.

Determination of Context Demographics

Various users and entities often find it useful to know about thedemographics of a particular context. Using demographic sensor data thatcan include implicit and explicit indications of various demographiccharacteristics of people and environments in particular contexts,various embodiments of the present disclosure can determine ademographic or demographic profile for the contexts. For example, FIG.14 illustrates contexts 1005 and 1015 that include a spatial component,e.g., an address, and a time component 1105-3, for which demographicsensor data has been received and/or collected. The demographic sensordata can include indications of demographic characteristics for peoplewithin each of the contexts. For the sake of clarity, the number ofimplicit and explicit indications of demographic characteristics shownin FIG. 14 has been limited. As shown, the demographic sensor data caninclude indications of a first demographic characteristic 1401, a seconddemographic characteristic 1402, and a third demographic characteristic1403. While described generically as individually numbered demographiccharacteristics, such demographic characteristics can include anyindividual demographic characteristic or combination of demographiccharacteristics. For example, the individually numbered demographiccharacteristics 1401, 1402, and 1403 can represent any combination ofquantifiable statistics for the people, such as age, sex, ethnicity,race, sexual preference, social class, social scene, and any otherimplicitly or explicitly determinable association with a particulargroup or classification.

By filtering the demographic sensor data determined to include or beassociated with context data that matches spatial and/or temporalcomponents of contexts 1005 and 1015, various embodiments of the presentdisclosure can determine demographic profiles for each context. Thedemographic profile for the context can include a complete listing ofthe available demographic details for each person in that context. Ifless granularity is required or desired, then a summary demographicprofile can be created. For example, based on the demographic sensordata, it can be determined that the demographics of context 1005 arepredominantly male. Similarly, it can be determined that thedemographics of context 1015 are predominantly female with an averageage greater than 55. The demographic profile for a particular contextcan then be output over various communication channels, e.g., publishedto a website, sent to groups of subscribing users via email or ShortMessage Service (SMS), or pushed to an application executed by mobileelectronic device.

Just as it is often useful to track changes in the emotion for acontext, it can also be useful to track changes in demographics for acontext. FIG. 15 illustrates a scenario 1500, in which changes in thedemographic profile of contexts 1005 and 1015 are observed from time1105-4 to time 1105-5. As shown, context 1005, e.g., the interior andexterior region around a bar at a particular intersection, begins attime 1105-4 being predominantly associated with demographic sensor datathat includes a particular demographic characteristic 1401. For example,demographic characteristic 1401 can be an indication of a male over theage of 40. Similarly, context 1015 at time 1105-4 can be determined tobe associated primarily with demographic sensor data that includesindications of the particular demographic characteristic 1403, e.g.,females around the age of 25. After some time period, at time 1105-5,the demographics of contexts 1005 and 1015 may change. As illustrated,context 1005 may now also be associated with demographic sensor datathat includes various instances of demographic characteristics 1401,1403, 1405, 1406, 1407, and 1409. The demographic sensor data of context1015 at time 1105-5 can shift to include a predominant mixture ofdemographic characteristic 1401 and 1402. Such shifts can indicate achange in the age, sex, ethnicity, or other demographic characteristicof the inhabitants or patrons of a particular context, i.e. the buildingor a business. The changes or trends in the demographic or demographicprofile of a context can then also be associated with the context andoutput over various communication channels.

Determination of Health and Wellness of a Context

Through the use of various types of individual and group health sensors,various embodiments of the present disclosure can determine the healthand wellness for various contexts. FIGS. 16 and 17 illustrate twoscenarios 1600 and 1700 of the same geographic region, e.g., a part of atown or city that includes a number of contexts. The contexts caninclude the group of buildings in context 1605, an outdoor park incontext 1615, and a particular building in context 1625 during someparticular time period, e.g., a week, month, or year. Accordingly,scenario 1600 in FIG. 16 can be associated with one particular timeperiod and scenario 1700 in FIG. 17 can be associated with anotherparticular time period. The time periods can overlap or be mutuallyexclusive.

By using the addresses, lot numbers, and/or the corresponding GPScoordinates of the locations located in contexts of scenario 1600 todefine the contexts, various embodiments can filter health sensor datareceived from multiple sensor enabled electronic devices 210 todetermine the health sensor data that includes context data that matchesor is associated with the contexts of interest. The health sensor datadetermined to include context data that matches each context can then beanalyzed to determine a health profile for the corresponding context.

Health sensor data received from health sensor enabled devicesthroughout scenario 1600 can be filtered to determine data that isassociated with contexts 1615 and 1625, and any other area or region ortime frame that a user or entity might be interested in as an individualor composite context. For example, context 1605 can be defined by theareas in and around the buildings associated with a particular range ofaddresses. The range of addresses can be used to determine the specificcoordinates of the geographic regions occupied by the buildings byreferencing a geographic map or a third-party mapping service. Context1615 can be defined by the name of the park, which can be used toreference some system of context descriptions, such as municipal surveydata, that defines the metes and bounds of the park with respect togeographical coordinates. Context 1625 can be defined by the block andlot number of the building or the name of the business that uses thebuilding in context 1625. Such semantically meaningful systems ofcontext descriptions can then reference an observable system of contextdescriptions to determine the limits of each context that will beobservable by sensor enabled devices. As with other embodiments of thepresent disclosure, health sensor enabled devices can include GPS,proximity-based, and other location determination and time determinationcapabilities. Accordingly, any health sensor readings obtained by thehealth sensor enabled devices can be associated with context data thatindicates the contexts in which the health sensor readings werecaptured.

The health profiles for contexts 1605, 1615, and 1625 can includevarious details about the health sensor data determined by health sensorenabled devices while the devices were within each context. For example,the health profile for contexts 1605, 1615, and 1625 can include acomplete listing of all implicit health sensor data and explicit userreported health data, such as health indications 1601, 1602, and 1603.In other embodiments, health profiles can include a summary or averageof the health indications present in the sensor data for a particularcontext 1605. In general, the health profile for each context can becustomized to analyze the health indications according to the needs of aparticular entity or user.

While the health indications 1601, 1602, and 1603 are listed as genericindications or descriptors of health of one or more people within thecontext, e.g., A, B, and C, embodiments of the present disclosureinclude any and all health and/or wellness descriptors determinable,observable, or inferable by health sensor enabled devices. For example,descriptors of health can include a description of body mass index(BMI), weight, blood pressure, blood sugar, heart rate, temperature,stress, or body fat content. Such descriptions can include numericalindexes or general/layman terms, such as underweight, normal weight,overweight, obese, and morbidly obese. Other descriptors of health caninclude explicit user reported data, such as vaccination status, mentalhealth status, feelings of wellness, disease and health history, etc. Insome embodiments, the health sensor data can also include environmentalsensor readings that describe or indicate the presence of toxins,poisons, pollution, and other helpful or harmful factors that can impactthe health of individuals that inhabit or use a particular context.

Accordingly, the health descriptors from the health sensor dataassociated with a context can be analyzed to produce default or customhealth profiles for that context. For example, context 1625 can includea restaurant. The summary of the health sensor data that includes healthindications 1601, 1602, 1603, and 1607, can be included in the healthprofile of the restaurant, e.g., overweight people eat at therestaurant. Similarly, the health profile associated with context 1615,that includes outdoor park space, can indicate that people who use thepark are generally physically fit and have low cholesterol.

While snapshot or cumulative health profiles for each context can beuseful for various purposes, is often useful to also track the changesin health profiles and/or health descriptors for specific contextsaccording to spatial or temporal changes. As discussed above inreference to emotion and demographic changes for specific contexts,embodiments of the present disclosure can also track changes in healthfor contexts. For example, scenario 1700 of FIG. 17 illustrates changesin health for contexts 1605, 1615, and 1625 relative to scenario 1600 ofFIG. 16. Specifically, the health profile associated with context 1605may change only slightly, if at all, if only limited changes in theassociated health descriptors in the health sensor data are observedbetween scenario 1600 and 1700. Meanwhile, the health profilesassociated with context 1615 and 1625 may change dramatically due to theobserved or determined differences in health descriptors in the healthsensor data associated with those contexts. Whereas the health profileassociated with context 1615 in scenario 1600 may have indicated thatphysically fit people frequented the park, the health profile associatedwith the context 1615 in scenario 1700 may indicate that the park is nowfrequented by people who smoke cigarettes or drink alcohol on a regularbasis. In contrast to the apparent decline in the health of context1615, the health profile of the restaurant in context 1625 may changefor the better. For example, the health indicators 1601 associated withcontext 1625 in scenario 1700 may now indicate that mostly physicallyfit people with low blood pressure patronize the restaurant.

As with other characteristic profiles, the health profiles of thevarious contexts can be output over various communication channels andmethods. For example, the health profile for the particular restaurantin context 1625 can be included in a restaurant review. Outputting thehealth profile for the context 1605 that includes a number of buildingsin a particular neighborhood can include generating a recommendation oran alert to real estate agents or public health department officialsthat the health for the context is in decline or is improving. Healthprofiles that indicate a decline or an increase in the general health orspecific health characteristics of individuals who inhabit or useparticular contexts can be used to indicate, analyze, and predictvarious environmental changes, epidemic changes, population changes, andother changes occurring within a context.

Particular embodiments may be implemented in a non-transitorycomputer-readable storage medium for use by or in connection with theinstruction execution system, apparatus, system, or machine. Thecomputer-readable storage medium contains instructions for controlling acomputer system to perform a method described by particular embodiments.The computer system may include one or more computing devices. Theinstructions, when executed by one or more computer processors, may beoperable to perform that which is described in particular embodiments.

As used in the description herein and throughout the claims that follow,“a”, “an”, and “the” includes plural references unless the contextclearly dictates otherwise. Also, as used in the description herein andthroughout the claims that follow, the meaning of “in” includes “in” and“on” unless the context clearly dictates otherwise.

The above description illustrates various embodiments along withexamples of how aspects of particular embodiments may be implemented.The above examples and embodiments should not be deemed to be the onlyembodiments, and are presented to illustrate the flexibility andadvantages of particular embodiments as defined by the following claims.Based on the above disclosure and the following claims, otherarrangements, embodiments, implementations and equivalents may beemployed without departing from the scope hereof as defined by theclaims.

What is claimed is:
 1. A method comprising: receiving, by a computersystem, demographic data from a plurality of distributed electronicdevices, wherein the demographic data comprises context data andcorresponding implicit demographic data sensed by the plurality ofdistributed electronic devices for a plurality of contexts; determining,by the computer system, a first context in the plurality of contexts;determining, by the computer system, a first portion of the demographicdata determined to include context data that matches the first context;analyzing, by the computer system, the implicit demographic data infirst the portion of the demographic data to generate a plurality ofdemographic characteristics for the first context; and generating, bythe computer system, a first demographic profile for the first contextbased on the plurality of demographic characteristics for the firstcontext.
 2. The method of claim 1, further comprising: generating, bythe computer system, an associated pair comprising the first context andthe first demographic profile; and outputting, by the computer system,the associated pair.
 3. The method of claim 1, wherein analyzing thefirst portion of the demographic data comprises mapping the implicitdemographic data in the first portion of the demographic data to theplurality of demographic characteristics.
 4. The method of claim 1,wherein the demographic data further comprises corresponding explicitdemographic data determined by the plurality of distributed electronicdevices for the plurality of contexts, wherein the explicit demographicdata comprises a plurality of user reported demographic characteristics,and wherein generating the first demographic profile is further based onthe plurality of user reported demographic characteristics in first theportion of the demographic data.
 5. The method of claim 1, wherein theplurality of distributed electronic devices comprises a plurality ofmobile electronic devices configured to sense the implicit demographicdata.
 6. The method of claim 5, further comprising: generating, by thecomputer system, a plurality of recommendations based on the firstdemographic profile for the first context, and sending, by the computersystem, the plurality of recommendations to a portion of the pluralityof mobile electronic devices, wherein the plurality of recommendationsare based on the demographic profile for the first context.
 7. Themethod of claim 6, further comprising: determining, by the computersystem, a second context in the plurality of contexts; determining, bythe computer system, a second portion of the demographic data determinedto include context data that matches the second context; analyzing, bythe computer system, the implicit demographic data in the second portionof the demographic data to generate a plurality of demographiccharacteristics for the second context; and generating, by the computersystem, a second demographic profile for the second context based on theplurality of demographic characteristics for the second context.
 8. Themethod of claim 7, further comprising updating the plurality ofrecommendations based on the second demographic profile for the secondcontext.
 9. The method of claim 8, wherein the first context comprises afirst time associated with a location, and the second context comprisesa second time associated with the location.
 10. The method of claim 7,wherein a difference between the first demographic profile and thesecond demographic profile describes a trend in demographics related tothe first context and the second context.
 11. The method of claim 6,wherein the pluralities of recommendations are further based ondemographic preferences associated with the portion of the plurality ofmobile electronic devices.
 12. The method of claim 5, wherein theplurality of distributed electronic devices further comprises aplurality of stationary electronic devices configured to sense implicitdemographic data, wherein each stationary electronic device isconfigured to sense implicit demographic data for a particular contextin the plurality of contexts.
 13. The method of claim 12, wherein thefirst portion of the plurality of distributed electronic devicescomprises a portion of the plurality of mobile electronic devices and aportion of the plurality of stationary electronic devices.
 14. Themethod of claim 13, wherein demographic data received from the pluralityof mobile electronic devices is weighted differently from demographicdata received from the plurality of stationary electronic devices inanalyzing the first portion of the demographic data to generate theplurality of demographic characteristics for the first context.
 15. Themethod of claim 1, wherein the first context comprises a dynamicallydetermined geographic location.
 16. The method of claim 1, wherein thefirst portion of demographic data further comprises a plurality ofconfidence scores, wherein analyzing the first portion of thedemographic data further comprises weighting the first portion ofdemographic data in response to the plurality of confidence scores togenerate the plurality of demographic characteristics for the firstcontext.
 17. The method of claim 1, wherein the context data isdetermined by the plurality of distributed electronic devices, andwherein the context data comprises a plurality of particular locations.18. The method of claim 17, wherein the context data further comprises aplurality of times.
 19. A non-transitory computer-readable storagemedium containing instructions that, when executed, control anelectronic device to be configured for: receiving demographic data froma plurality of distributed electronic devices, wherein the demographicdata comprises context data and corresponding implicit demographic datasensed by the plurality of distributed electronic devices for aplurality of contexts; determining a first context in the plurality ofcontexts; determining a first portion of the demographic data determinedto include context data that matches the first context; analyzing theimplicit demographic data in first the portion of the demographic datato generate a plurality of demographic characteristics for the firstcontext; and generating a first demographic profile for the firstcontext based on the plurality of demographic characteristics for thefirst context.
 20. An electronic device comprising: a processor; ademographic sensor; an electronic communication interface; and anon-transitory computer-readable storage medium containing instructions,that when executed, control the processor to be configured to: activatethe demographic sensor to determine a demographic sensor reading;determine context data for the demographic sensor reading, wherein thecontext data describes the circumstances in which the demographic sensorreading was determined; generate demographic sensor data comprising thecontext data and the demographic sensor reading; send the demographicsensor data to one or more remote service providers through theelectronic communication interface; and receive, from a first remoteservice provider in the one or more remote service providers through theelectronic communication interface, summary demographic sensor data fora particular context, wherein the summary demographic sensor datacomprises demographic sensor data, received by the first remote serviceprovider from a plurality of other electronic devices, and determined toinclude context data that matches the particular context.